Abstract
A longitudinal study refers to collection of a response variable
and possibly some explanatory variables at multiple follow-up times. In
many clinical studies with longitudinal measurements, the response variable,
for each patient is collected as long as an event of interest, which considered
as clinical end point, occurs. Joint modeling of continuous longitudinal
measurements and survival time is an approach for accounting association
between two outcomes which frequently discussed in the literature, but design
aspects of these models have been rarely considered. This paper uses
a simulation-based method to determine the sample size from a Bayesian
perspective. For this purpose, several Bayesian criteria for sample size determination
are used, of which the most important one is the Bayesian power
criterion (BPC), where the determined sample sizes are given based on BPC.
We determine the sample size based on treatment effect on both outcomes
(longitudinal measurements and survival time). The sample size determination
is performed based on multiple hypotheses. Using several examples, the
proposed Bayesian methods are illustrated and discussed. All the implementations
are performed using R2OpenBUGS package and R 3.5.1 software.
